Efficient interatomic descriptors for accurate machine learning force fields of extended molecules
Abstract Machine learning force fields (MLFFs) are gradually evolving towards enabling molecular dynamics simulations of molecules and materials with ab initio accuracy but at a small fraction of the computational cost. However, several challenges remain to be addressed to enable predictive MLFF sim...
Main Authors: | Adil Kabylda, Valentin Vassilev-Galindo, Stefan Chmiela, Igor Poltavsky, Alexandre Tkatchenko |
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Format: | Article |
Language: | English |
Published: |
Nature Portfolio
2023-06-01
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Series: | Nature Communications |
Online Access: | https://doi.org/10.1038/s41467-023-39214-w |
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